JP6594826B2 - Vehicle diagnostic device - Google Patents

Description

  The present invention relates to a vehicle diagnostic apparatus.

  Patent Document 1 discloses “a vehicle failure detection device, an electronic control unit, and a vehicle failure detection method capable of specifying a failure even if the signal value does not satisfy a standard in relation to a threshold value while suppressing erroneous detection”. It is disclosed.

JP 2011-79350 A

  In the vehicle diagnosis, it is conceivable to perform some kind of determination on various types of signal data of the vehicle, and to perform a diagnosis for deriving the contents of the vehicle malfunction or the like based on the determination result. In order for this vehicle diagnosis to be performed appropriately, it is desirable that the determination result be performed based on the characteristics of the signal change.

  Patent Document 1 does not describe anything about determining the characteristics of a signal change and making a vehicle diagnosis based on the determination result.

  Therefore, an object of the present invention is to provide a technique for appropriately diagnosing a vehicle state based on the characteristics of signal change.

  The present application includes a plurality of means for solving at least a part of the above-described problems. Examples of the means are as follows. In order to solve the above-described problems, a vehicle diagnostic apparatus according to the present invention is a vehicle diagnostic apparatus that uses vehicle signal data that is time-series data, and ranks the signal data in an arbitrary period for each signal item. And a diagnosis processing unit for diagnosing driving and vehicle status based on a diagnosis rule having a logical structure from a rank determination result in the signal item, and the signal data is a signal feature. Converted into symbol data using signal feature determination management reference information for dividing into quantities, and divided into the signal feature quantities with a time range based on a time-series sequence of the symbol data. The occurrences of signal feature values are totaled, and the total result is rank-determined using rank determination management reference information, or the symbol data itself is used as the rank determination result.

  According to the present invention, it is possible to appropriately diagnose the vehicle state based on the characteristics of the signal change. Problems, configurations, and effects other than those described above will be clarified by the following description of embodiments.

It is the figure which showed an example of the vehicle diagnostic system using the vehicle diagnostic apparatus which concerns on this invention. It is the figure which showed the functional block example of the vehicle acquisition apparatus which the data acquisition apparatus and data center which are mounted in a vehicle have. It is the figure which showed the example of signal data of a vehicle. It is a frequency distribution of the signal data shown in FIG. It is the 1 of the figure explaining a signal division | segmentation method. It is the 2 of the figure explaining a signal division | segmentation method. It is the 3 of the figure explaining a signal division | segmentation method. It is the 4 of the figure explaining a signal division | segmentation method. It is the 5 of the figure explaining a signal division | segmentation method. It is the figure which showed the data structural example of the signal feature-value. It is a figure explaining determination of the difference of signal data. The result of dividing the signal data 1 of FIG. 11 by change non-division is shown. It is the figure which showed the example of totalization of the signal feature-value of signal data. FIG. 14 is a diagram illustrating a frequency distribution (histogram) of the number of signal feature ranks in the total result example of FIG. 13. It is the figure which showed the example of the relationship between a monitor item and an operation item. It is the figure which showed the example of the relationship between the operation item of FIG. 15, and a characteristic. It is the figure which showed the example of a transition of an operation state. It is the figure which showed the data structural example of the rank determination result. It is the figure which showed the example of the signal feature-value used as a diagnostic object. It is the figure which showed the example of the signal feature-value of two signal items used as a diagnostic object. It is the figure which showed the example of the diagnostic report. It is the flowchart which showed the processing example of the signal division method. It is the flowchart which showed the example of a diagnostic process. It is the flowchart which showed the example of a diagnostic process. It is the flowchart which showed the example of a diagnostic process. It is the figure which showed the hardware structural example of the vehicle diagnostic apparatus.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  The vehicle diagnosis apparatus manages the good mechanical operation of the vehicle in operation, the good or bad characteristics of the driver's driving, the characteristics, the deterioration of the machine parts, the malfunction, and the like. The vehicle diagnostic apparatus determines the degree of abnormality of the signal item based on the characteristics of the change of the signal item by using various signal data or waveforms monitoring the driving situation of the vehicle. The vehicle diagnostic device diagnoses machine operation quality, driving characteristics, and component failures, and outputs a diagnosis result. Management reference information for determination and definition information for diagnosis are set in advance.

  The vehicle diagnosis apparatus divides signal data into signal feature amounts to the extent necessary for determination, diagnosis, and analysis for determining the degree of abnormality from the signal data for diagnosis. The signal feature amount is a straight line segment in which the horizontal axis is time and the vertical axis is a signal value and is symbolized to divide the time range. The vehicle diagnosis apparatus determines a totaling result obtained by summing up signal feature amounts or a signal feature amount itself. The determination result is defined as a set of a vehicle state, a signal item, a division method that is an algorithm of feature division, a tabulation method or a signal feature amount, and a degree of signal value magnitude or an abnormality degree state that is a determination result.

  The definition information of diagnosis is a diagnosis rule composed of a relationship between a condition and a result. In the condition, a condition for the determination result of determining the feature is defined. The determination result and the diagnosis result include a time range corresponding to a signal that is time-series data.

  In a mobile body such as an automobile (hereinafter sometimes referred to as a vehicle), an ECU (Electronic Control Unit) for controlling driving is used. Various signals of the vehicle are monitored by an ECU, for example. Specifically, various sensors for improving safety are mounted on the vehicle, and the ECU monitors their output signals. The ECU similarly monitors the signal of the actuator that operates the vehicle. The ECU monitors a sensor mounted on the vehicle and performs fuel management such as exhaust gas. The same technology is applied not only to small cars and ordinary cars, but also to trucks of trucks and buses of passenger cars. The same applies to special industrial vehicles such as construction machinery and agricultural machinery.

  The ECU handles various signals for controlling a target device. The ECU is mounted not only for the engine but also for controlling transmissions, various chassis devices, and electrical components. Various ECUs cooperate with each other via a CAN (Controller Area Network) to control the operation of the vehicle. Various kinds of signal data exist in the vehicle.

  A vehicle is equipped with a digital tachometer, a drive recorder, and a GPS (Global Positioning System) that manages and stores various signals. The vehicle wirelessly transmits data to a remote server, and the vehicle data is also stored in a large capacity storage device.

  The signal data is also called sensor data. The signal data is time-series data generated with the passage of time, and the operation of the device can be determined by finding data fluctuations and patterns along the time. The signal data is device operation information, and an abnormality or failure of the device can be detected based on the value and its variation.

  The vehicle diagnosis device uses various types of signal data of the vehicle to detect an abnormality and determine the degree of abnormality, and diagnoses such as identifying the part to be replaced and the maintenance method as well as the status of the malfunction and the malfunction. Realize. The ECU is equipped with a sensor, an actuator, and a self-diagnosis device that detects abnormality of the ECU itself. The ECU monitors the signal value and detects an abnormality according to a comparison result with a threshold value preset in the microcomputer. In this case, the correctness of detection of an abnormality corresponding to a failure depends on the threshold value.

  When abnormality detection of signal data is performed by the ECU, the detected result is accumulated in a storage device such as a memory mounted on the ECU. Even if the signal data itself is stored, it takes space (spatial size) and cost to increase the capacity of the storage device. Even if signal data in a short time unit of the second or millisecond of the ECU is communicated, there is a cost related to communication capacity, necessary devices, and equipment.

  The vehicle diagnosis device uses a variety of signal data of the vehicle to determine whether there is an abnormality and diagnoses the contents of the malfunction or failure. The process is automated, and the diagnosis result is transmitted as a report to, for example, a driver, a company that owns the vehicle, or a service company that performs maintenance.

  The vehicle diagnosis apparatus divides signal data as feature quantities in a time interval from the viewpoint of whether or not there is a change, for abnormality determination and diagnosis. The feature amount is symbolized by direct determination of the signal value, or is determined by the aggregated result. The state of the vehicle or the like is diagnosed from the determination result. Therefore, the vehicle diagnosis apparatus diagnoses the vehicle from the continuous time-series feature amounts without missing a plurality of signals. Therefore, it means that the vehicle can be diagnosed by storing this characteristic amount in the storage device and transmitting it.

  That is, the vehicle diagnosis apparatus divides the signal data into signal feature amounts without losing time information in time series, and ranks the signal data. The vehicle diagnosis apparatus can determine the vehicle state based on a combination of signal feature amounts in a time range. Further, by using the signal feature amount, the vehicle diagnosis apparatus can count the number of appearances of the signal feature amount in the time range, and can also determine the length of the signal feature amount.

  The vehicle diagnosis apparatus defines a diagnosis rule that derives a diagnosis result from the determination result and the diagnosis result, with the determination result as a combination of a vehicle state, a signal item, a division method, a feature, and a state that is a determination result. The vehicle diagnosis device uses the information for determination to detect an abnormality from the signal as management reference information, and the diagnostic rules using the driver's driving characteristics (癖), failure contents, breakdowns, and maintenance contents as diagnosis results Thus, diagnosis know-how and knowledge can be expressed formally by associating the determination result with the formal correspondence. It can be added and edited as formal knowledge data.

  The vehicle diagnosis apparatus performs abnormality detection and diagnosis using the signal feature amount. The signal feature amount is data having whether or not there is a change as a component, and the data capacity is reduced as compared with the signal data. In the case of the signal feature amount format, the data storage capacity is small, and the burden of data transmission is reduced. Therefore, according to the vehicle diagnostic apparatus, in the diagnosis of a vehicle that is expected to increase the number of objects to be diagnosed, it is possible to store data in a storage device outside the vehicle and make a diagnosis with a computer.

  FIG. 1 is a diagram showing an example of a vehicle diagnostic system using a vehicle diagnostic apparatus according to the present invention. As shown in FIG. 1, the vehicle diagnostic system includes a data center 1 having a vehicle diagnostic device (not shown), vehicles 2 and 3, a customer company 4, a service company 5, and a network N. Yes.

  The normally operating vehicle 2 transmits signal feature quantities to the data center 1 via the network N by radio. For the normally operating vehicle 2, in the data center 1, a computer (vehicle diagnosis device) periodically performs a diagnosis process to generate a diagnosis report.

  Similarly, the vehicle 3 in which an abnormality has occurred transmits a signal feature amount to the data center 1. For the vehicle 3 in which an abnormality has occurred, in the data center 1, the computer immediately performs a diagnosis process and generates a diagnosis report.

  The communication from the vehicles 2 and 3 is wireless communication, but the communication method is not limited to, for example, a mobile phone line, and any communication method capable of achieving this method is possible. The method is arbitrary. The present invention is not limited to the communication method. In addition, transmission / reception of wireless communication is performed at an arbitrary place or facility such as a base station, for example. The data center 1 does not necessarily have to perform wireless communication. Communication between the data center 1, the customer company 4, and the service company 5 does not have to be wireless, and may be the Internet, a telephone line, and a sanitary line. The form of the network N is not limited.

  The generated diagnostic report is transmitted to the customer company 4, and the owner, driver, asset manager, etc. of the vehicle confirm the contents, and take judgment and action according to the diagnostic result.

  The diagnosis report is transmitted to the service company 5 for maintenance, maintenance, and parts sales. The person in charge of maintenance and maintenance makes inquiries to the owner and driver of the vehicle and prepares for repairs. In addition, the service company 5 checks and arranges parts inventory used for repair.

  The vehicles 2 and 3 are provided with a data acquisition device to be described later. The data acquisition device has a function of acquiring signal data such as an ECU and transmitting the signal data to the outside.

  In the data center 1, for example, a vehicle diagnosis device is installed in the form of a computer. The vehicle diagnostic device acquires data of the vehicles 2 and 3 and makes a numerical determination to diagnose a vehicle malfunction, a method of dealing with the failure, and the like, and generates a diagnostic report.

  FIG. 2 is a diagram illustrating an example of functional blocks of the data acquisition device mounted on the vehicles 2 and 3 and the vehicle diagnosis device included in the data center 1. As shown in FIG. 2, the data acquisition device 10 mounted on the vehicles 2 and 3 includes a communication processing unit 11, a signal data acquisition unit 12, a feature division processing unit 13, and a signal feature determination management reference information DB (DB). : Date Base) 14.

  The vehicle diagnosis apparatus 20 included in the data center 1 includes a communication processing unit 21, a determination processing unit 22, a diagnosis processing unit 23, a storage unit 24, and a report generation unit 25. The determination processing unit 22 includes a signal feature aggregation processing unit 22a, an operation state diagnosis processing unit 22b, and a rank determination processing unit 22c. The diagnosis processing unit 23 includes a determination result acquisition unit 23a and a diagnosis unit 23b. The storage unit 24 includes a feature amount data DB 24a, a rank determination management reference information DB 24b, a diagnosis rule 24c, and a driving state determination rule DB 24d.

  The communication processing unit 11 of the data acquisition device 10 communicates with an in-vehicle device such as an ECU. The communication processing unit 11 communicates with the vehicle diagnostic device 20.

  The signal data acquisition unit 12 acquires vehicle signal data received by the communication processing unit 11.

  The feature division processing unit 13 divides the signal data acquired by the signal data acquisition unit 12 into signal feature amounts. The process of dividing the signal feature amount includes an operation of symbolizing the signal by rank determination. For this determination, the signal feature determination management reference information DB 14 stores information for symbolizing signal data. The storage device may be a flash memory incorporated in a microcomputer or a storage device mounted on a microcomputer board.

  The communication processing unit 11 transmits the signal feature amount processed by the feature division processing unit 13 to the vehicle diagnosis apparatus 20.

  The communication processing unit 21 of the vehicle diagnostic device 20 communicates with the data acquisition device 10. The communication processing unit 21 stores (accumulates) the signal feature amount transmitted from the data acquisition device 10 in the feature amount data DB 24a. The communication processing unit 21 communicates with the customer company 4 and the service company 5.

  In the diagnosis of the vehicle, first, the determination processing unit 22 performs rank determination processing of the signal feature amount stored in the feature amount data DB 24a. Since the signal feature amount is symbolized data, the signal feature amount can be used as a rank determination result of the signal item. However, when it is desired to assign a name of another state to the symbol used in the signal feature amount division processing, the rank determination processing unit 22c performs rank determination to determine the state. The rank determination processing unit 22c also determines the rank for the time range (length) of the feature amount. Management criterion information such as a threshold value is necessary for rank determination, and the threshold value is stored in the rank determination management criterion information DB 24b.

  Aggregation such as the number of occurrences of the signal feature amount of the same symbol and the average, maximum, and minimum of the time range is performed by the signal feature aggregation processing unit 22a. The rank determination of the total value is performed by the rank determination processing unit 22c.

  The driving state diagnosis processing unit 22b determines a vehicle state such as stopping, traveling, accelerating, turning left, turning right from the signal feature amount. This is because the vehicle diagnosis is performed according to the driving state in the subsequent diagnosis process. The driving state diagnosis processing unit 22b may select a management criterion for rank determination according to the driving state. The driving state diagnosis processing unit 22b refers to the driving state determination rule stored in the driving state determination rule DB 24d when performing the driving state diagnosis.

  The processing result of the determination processing unit 22 is the signal feature amount itself or the rank determination result of the signal feature amount and the time range of the signal feature amount. In addition, the processing result of the determination processing unit 22 is obtained by adding driving information to the rank determination result of the signal feature amount aggregation result.

  The determination result acquisition unit 23 a of the diagnosis processing unit 23 acquires the rank determination result of the determination processing unit 22.

  The diagnosis unit 23b derives a diagnosis result such as a malfunction or failure of the vehicle from the rank determination result acquired by the determination result acquisition unit 23a. Diagnosis is based on inference processing using diagnosis rules. The diagnosis rule is defined in the form of IF-THEN, for example. The diagnosis rule expresses a one-layer logical relationship that results in the THEN part if the condition of the IF part is satisfied. The diagnosis rule is a diagnosis rule for deriving a diagnosis result from the determination result, and a diagnosis rule for deriving a diagnosis result from the diagnosis result, and is stored in the diagnosis rule DB 24c.

  The report generation unit 25 generates a diagnosis report based on the rank determination result of the determination processing unit 22 and the diagnosis result of the diagnosis processing unit 23. The generated diagnostic report is transmitted to the customer company 4 or the service company 5 via the communication processing unit 21.

  The determination result, the diagnosis result, or the diagnosis report is generated in the process. The system configuration may include a database for storing and accumulating these. As described above, the vehicle diagnosis apparatus 20 can automatically perform vehicle diagnosis using the signal data of the vehicle and notify the customer of the diagnosis report.

  The signal feature amount will be described.

  FIG. 3 is a diagram showing an example of vehicle signal data. In FIG. 3, the horizontal axis indicates time, and the vertical axis indicates engine speed. FIG. 3 shows the engine speed of the time series of one day.

  The signal data in FIG. 3 is sampled every minute, and the total number of data is “60 × 24 = 1440 cases”. From the graph, the engine speed frequently occurs at about “500 rpm”, “800 rpm”, and “1500 rpm”.

  Further, in the example of the engine speed in FIG. 3, there is a portion that continues for a certain time range with a small variation. The graph shows vertical dashed lines where major changes have occurred and where there have been no major changes.

  For example, a section A1 shown in FIG. 3 indicates that the engine is continuously operated at a rotation speed “1500 rpm” in a time range of about one and a half hours. In section A1, it can be presumed that the vehicle is traveling at high speed from the high rotational speed.

  FIG. 4 is a frequency distribution of the signal data shown in FIG. The horizontal axis in FIG. 4 indicates the rotational speed, and the vertical axis indicates the frequency.

  In the example of the graph shown in FIG. 4, the frequency of a specific signal value is often generated, as in the portion of the rotation speed “1500 rpm”. Such a place is, for example, a place continuous in a time range in FIG.

  That is, as shown in section A1 in FIG. 3, the characteristics of the driving signal can be understood by dividing the signal into time ranges depending on whether or not the signal has changed. Even if the signal value is not constant in a certain time range and changes variously, if the signal value is divided at the time of the change, it is possible to infer the characteristics of driving from the amount of change. Therefore, the feature division processing unit 13 divides the signal data in a time range depending on whether or not there is a change and sets it as a signal feature amount.

  A signal division method for obtaining a signal feature amount from signal data will be described.

  FIG. 5 is a first diagram illustrating a signal division method. FIG. 5 illustrates an example of symbolization based on signal feature determination management criteria of signal data. The horizontal axis in FIG. 5 indicates time, and the vertical axis indicates the signal value.

  The signal is a signal value represented by a point at a time point in time series, as indicated by an arrow A11 in FIG. Further, as shown by an arrow A12 in FIG. 5, a signal can be regarded as a line segment or a line segment line by connecting time points in time series.

  The feature division processing unit 13 symbolizes the signal value by determining the rank. Here, the rank for determining the rank of the signal value is set by a threshold value stored in the signal feature determination management reference information DB 14. This rank determination is called signal feature rank determination. Hereinafter, the rank of signal feature rank determination may be referred to as a signal feature rank.

  The threshold values are set in 9 levels from A, B, C,. For example, the threshold value is indicated by a horizontal broken line as indicated by an arrow A13 in FIG. The signal feature rank is within each threshold range, that is, between the broken lines in the vertical axis direction.

  In FIG. 5, the signal feature rank determination result of the signal is shown in lower case letters of the alphabet corresponding to the signal feature rank {A, B, C, D, E, F, G, H, I}. For example, the signal is “a, a, b, b, b, c, f, g, g, i, i, i, h, h, h, c, b, b, b, b, b, e, i, g, a, b, c, h, i, i, i, i, i, i, i, i, i, h, i, i, e, b, b, d, i, i, i, i, i, i ”.

  FIG. 6 is a second diagram illustrating the signal division method. The horizontal axis in FIG. 6 indicates time, and the vertical axis indicates the signal value. FIG. 6 shows a result in a case where a continuous range (continuous) in which the symbols do not change in time series is acquired.

  For example, in FIG. 6, the same symbol sequence is connected with a solid line between the points in FIG. 5. Moreover, in FIG. 6, the place where the symbol changed is connected with the dashed-dotted line. For example, since two “a” s continue from the left of the horizontal axis, a signal string “a2” is obtained. Further, a signal string “b3” is obtained following “a2”.

  That is, FIG. 6 illustrates a signal sequence with symbols and points in the section. As indicated by arrows A13 and A14 in FIG. 6, a signal string such as “b5” of the signal string and “i9” of the signal string is obtained. Thus, it can be seen that the range in which the symbols are continuously arranged in time series is a signal in the time range such as the section A1 in FIG. A signal sequence having no such change is a signal feature amount. This signal waveform division method is changeless division.

  FIG. 7 is a third diagram illustrating the signal division method. In FIG. 7, the horizontal axis represents time, and the vertical axis represents the signal value. FIG. 7 shows a result in a case where a continuous range (series) in which symbols change in time series is acquired.

  For example, in FIG. 7, the points where the symbols change in time series are connected with a solid line in FIG. 5. Moreover, in FIG. 7, the continuous part of the same symbol without a change is connected with the dashed-dotted line. For example, from the left of the horizontal axis, there is a single signal sequence “a”, and then there is a signal sequence “ab2” composed of two points “a” and “b”. Then, there is a signal sequence of “b” at one point, and there is a signal sequence of “bg4” where the symbol continues to change at four points from “b” to “g”.

  A signal sequence having a change can be expressed by a start symbol, an end symbol, and a score of the section. Also, the points painted black are points that change in the vertical direction, and the signal sequence is divided at these points. For example, the signal strings of arrows A15 and A16 shown in FIG. 7 are originally “b, e, i, g, a”, and the symbols are changed, but “b, e, i” is increased, “i , G, a ”are descending. Therefore, the signal sequence indicated by the arrow A15 is “bi3”, and the signal sequence indicated by the arrow A16 is “ia3”. Similarly, the signal sequence indicated by the arrow A17 is “ai5”, but the symbol next to the last “i” is “i”, and is divided because there is no change. A signal sequence having such a change is also a signal feature amount. This signal splitting method is variable splitting.

  FIG. 8 is a fourth diagram illustrating the signal division method. In FIG. 8, the horizontal axis indicates time, and the vertical axis indicates the signal value. In FIG. 8, all signals are divided by a signal sequence of two or more points. That is, the signal sequence of FIG. 8 can be obtained by superimposing all the signal sequences of one point as shown in the non-variable division of FIG. 6 and the variable division of FIG. All data is “a2, ab2, b3, bg4, g2, gi2, i3, ih2, h3, hb3, b5, bi3, ia3, ai5, i9, ih2, hi2, i2, ib3, b2, bi3, i6” Are divided by 22 signal feature quantities.

  FIG. 9 is a fifth diagram illustrating the signal division method. In FIG. 9, the horizontal axis represents time, and the vertical axis represents the signal value. In FIG. 9, the signal is divided only by the data at the time of the presence or absence of the change.

  The signal feature amount is a line segment determined by repeating one point of data when there is no change, and is a line segment connecting the start point and the end point when there is a change. Therefore, the signal based on the signal feature amount can be represented as a line segment as shown in FIG.

  Note that the signal values at the start point and the end point exist as data. In the case of only symbols representing ranks, the signal value is represented as a signal by, for example, a horizontal line segment that passes through the center of the range that determines the rank and a line segment that connects the centers of the ranges of different ranks. The signal value may not be the center, but may be a lower limit value, an upper limit value, or a representative value determined in advance for each rank.

  The above is the signal dividing method from the signal data to the signal feature amount. The signal feature amount of the signal data processed by the feature division processing unit 13 is stored in the signal feature determination management reference information DB 14. The signal feature amount stored in the signal feature determination management reference information DB 14 is periodically transmitted to the vehicle diagnostic apparatus 20 by the communication processing unit 11, for example.

  FIG. 10 is a diagram illustrating a data configuration example of the signal feature amount. As shown in FIG. 10, the signal feature amount is indicated by a set of “tm, tr | te, c1 | c2, vs, ve, is”. The vertical bar “|” means selection of “any”. The symbol “c1” is a symbol without change, for example, {A, B, C,. The two symbols “c2” are a set of symbols with changes, for example, {AB, AC,..., BA, BC,. The two symbols may be {AA, AB, AC,..., BA, BB, BC,. If the signal feature amount is determined by the time range and the representative value corresponding to the symbol, the minimum data configuration of the signal feature amount can be “tm, tr, c1 | c2”.

  If the signal data is divided into signal feature amounts, the vehicle diagnosis apparatus 20 can total the symbols, combinations of symbol changes, and time ranges. And the vehicle diagnostic apparatus 20 can determine the difference of signal data.

  FIG. 11 is a diagram illustrating determination of a difference in signal data. In FIG. 11A, signal data 1 is shown. In FIG. 11B, signal data 2 is shown. The horizontal axis indicates time, and the unit is “minute”. The vertical axis represents the signal, and the unit system is an arbitrary “au (arbitrary unit)”.

  The signal data 2 shown in (b) of FIG. 11 is a signal that is repeated four times by shortening the signal data 1 shown in (a) of FIG. 11 by ¼. As described above, the signal feature amount is obtained by dividing the signal based on whether or not there is a change. The number of appearances of signal values of individual signal data is the same for signal data 1 and signal data 2 except for variations. However, the number of appearances of the signal feature amount differs between the signal data 1 and the signal data 2. The vehicle diagnosis apparatus 20 can quantitatively determine the difference between the signal data 1 and 2 by using the signal feature amount.

  FIG. 12 shows the result of dividing the signal data 1 of FIG. The signal value exists in a range from “−10” to “110”, and the threshold value is set in increments of “−10” to “10”. Ranks that are signal feature ranks are A, B, C to L in ascending order of signal values. The signal data 2 can be similarly divided.

  Note that the signal feature amounts of the signal data 1 and 2 as shown in FIG. 10 are obtained by the change non-division and change-with-division processes by the feature division processing unit 13 of the data acquisition device 10 as described above. The obtained signal feature amounts of the signal data 1 and 2 are transmitted to the vehicle diagnostic device 20, and the signal feature summation processing unit 22 a of the vehicle diagnostic device 20 sums up the signal feature amounts transmitted from the vehicle diagnostic device 20. .

  FIG. 13 is a diagram illustrating an example of a result of summing signal feature amounts of the signal data 1 and 2. “Data1” illustrated in FIG. 13 indicates a totaling result of the signal data 1. “Data2” indicates a totaling result of the signal data 2.

  Each row in FIG. 13 shows the total result of signal feature ranks from “A” to “L”. FIG. 13 shows an example in which the number of signal feature ranks (count), the average length of signal feature amounts (average), and the maximum length (max) of signal feature amounts are tabulated.

  The lower limit (LOWER) and the upper limit (UPPER) in FIG. 13 mean the range of signal feature rank determination (above and below). In addition, in the signal feature rank, if the calculated value is not obtained, for example, the average cannot be calculated because there is no signal feature amount, “NaN” is described.

  FIG. 14 is a diagram showing a frequency distribution (histogram) of the number of signal feature ranks in the total result example of FIG.

  As shown in FIG. 14, the number of appearances of the signal feature rank of the signal data 2 (data 2) is clearly larger than the number of appearances of the signal feature rank of the signal data 1 (data 1). That is, it can be seen that the signal data 2 has more signal changes than the signal data 1.

  For example, if the signal data 1 and 2 are accelerator depression signals, it can be estimated that the speed is frequently changed. Further, it can be seen from the numerical values of “average” and “max” in FIG. 13 that the length of the signal feature amount, that is, the time is shorter in the signal data 2.

  Thus, the signal feature totalization processing unit 22a of the determination processing unit 22 of the vehicle diagnosis apparatus 20 totals signal feature amounts. Thereby, the vehicle diagnostic apparatus 20 can determine the difference in the signal data based on the total result of the signal feature amount. That is, the vehicle diagnosis apparatus 20 can perform rank determination (different from the above-described signal feature rank determination) on the signal feature amount or the totaled result as described below.

  The rank determination will be described. The rank determination processing unit 22c rank-determines the signal feature amount or the total result. The rank determination result may include the signal feature rank determination result itself. The target of rank determination is the signal feature rank determination result of the signal feature amount (rank determination result based on the signal feature determination management reference information) and the time range or length of the signal feature amount. The target of rank determination is also the amount of change and inclination from the start to the end of the signal feature amount. Further, the target of rank determination is also the result of signal feature rank determination and the result of total length.

When performing rank determination, the rank determination processing unit 22c first determines a determination time range in order to identify a signal feature quantity to be determined. The start time of the determination process is “T ^st ”, and the end time is “T ^en ”. Start of the signal feature quantity of the index is "#i"^"t _{st #i",} with respect to end ^"t _{en #i",} for example, the following equation (1) is satisfied.

T ^st ≦ t ^st _#i <T ^en (1)

The end time “t ^en _#i ” may also be included in this range. Note that the time information of the signal feature amount may be indicated not by the start and end, but by the start time point “t ^st _#i ” and the length indicated by the following equation (2).

^{_{len #i = t en #i -t st}} #i +1 ... (2)

  When determining the signal feature rank determination result of the signal feature amount, the rank determination result may be associated with the set of signal feature ranks. For example, it is assumed that the signal feature rank is defined by alphabets “A” to “L” {A, B, C, D, E, F, G, H, I, J, K, L}. When the signal feature quantity without change (for example, the symbol “c1” shown in FIG. 10) is ranked in three states {LOW, MIDDLE, HIGH}, {A, B, C, D} is set to LOW. , {E, F, G, H} are set to MIDDLE, {I, J, K, L} are set to HIGH, and so on. More specifically, if the signal feature amount is “a3, d8”, the rank is determined as “LOW”, and if “f10, g8”, the rank is determined as “HIGH”.

  Further, for example, in the case where a signal characteristic amount with change (for example, the symbol “c2” shown in FIG. 10) is ranked in two {NORMAL, LARGE}, {AJ, AK, AL, BK, BL, CL} is set to “LARGE” and the others are set to “NORMAL” or the like. In this case, if the signal feature amount is “cd2”, the rank is determined as “NORMAL”, and if “bk13”, the rank is determined as “LARGE”.

  Alternatively, the set may be determined by a direct product of the start and end signal feature ranks. For example, if the signal characteristic rank of “start × end” is {A, B, C} × {J, K, L}, {AJ, AK, AL, BJ, BK, BL, CJ, CK, CL }.

  The signal feature rank determination result itself of the signal feature amount may be the result of rank determination. In this case, since the rank determination processing unit 22c can handle the signal feature rank determination result symbolically, it is not always necessary to determine the rank of the signal feature amount. The rank determination result is the same symbol as the signal feature rank determination result.

  When determining the rank (time range) “len # i” of the signal feature quantity, the rank determination processing unit 22c associates the rank determination result with the time range or the number of continuous data points. For example, ignoring the time unit system, if rank determination is made into three states of {SHORT, MEDIUM, LONG} of "SHORT" less than 5 and "MEDIAUM" less than 50 and "LONG" more than 50 The signal feature values “a3, cd3” are “SHORT”, “d8, f33” is “MEDIAUM”, and “bk53, l70” is “LONG”.

  The change in the signal feature amount is classified (rank determination) into an upper change and a lower change. Since the difference between the rank of the signal feature rank determination at the start time and the rank at the end time can be quantified by the rank of the change in the order of the symbols, the change in the signal feature amount is positively or negatively distinguished from the upper side and the lower side. Can. In this way, the upper and lower changes in the signal feature amount are quantified, so that the rank can be determined numerically. If there is a signal value at the start time point and the end time point, the upper change and the lower change can also be ranked by the difference between the signal values.

  The inclination of the signal feature amount is classified into an upward inclination and a downward inclination. The inclination of the signal feature amount can be quantified by dividing the difference between the rank of the signal feature rank determination at the start time and the rank at the end time by the length “len # i”. The slope of the signal feature amount can be determined to be positive or negative and upward or downward. The slope of the signal feature quantity can be quantified by dividing the difference between the signal values at the start time and the end time by the length “len # i”, so that the rank can be determined.

  The rank determination of the counting result will be described. First, the total calculation of signal feature amounts will be described. The aggregation calculation is performed by the signal feature aggregation processing unit 22a. Aggregation calculation is, for example, the number of occurrences for each signal feature rank determination result, the average of signal feature amount length (time range) for each signal feature rank determination result, its standard deviation (variation), and its minimum and maximum And is the target. The signal feature totalization processing unit 22a limits the signal feature amount to be totaled to the time range represented by Expression (1) during the totalization process.

The tabulation results are as shown in FIG. 13, for example. The number of appearances for each signal feature rank determination result in the signal data 1 and the signal data 2 is shown in the column “count”. The average length is shown in the “average” column and the maximum is shown in the “max” column. Since the length is a numerical value, the standard deviation and the minimum are calculated in the same way. The aggregation result is not limited to the number of appearances, the average, the standard deviation, the minimum, and the maximum. For example, the aggregation result may be a statistic such as a median, mode, kurtosis, and skewness, or may be calculated by a function. Aggregate result of the start time of calculation "t ^st" and end time "t ^en", if the Scope of the range of rank determination process, the start time "T ^st" and the end time is "T ^en".

  The aggregation result is a numerical value. For example, when the number of appearances is determined to rank in three states {FEW, NORMAL, MANY}, if “3” is “FEW”, 3 to 6 is “NORMAL”, and 6 or more is “MANY”, the number of occurrences “ “5” is a rank determination result of “NORMAL”, and the appearance number “10” is a rank determination result of “MANY”. The total number of signal feature amounts may be set in the same manner as the signal feature amount length. The above is the description of rank determination.

  The driving state of the vehicle will be described. In the diagnosis of a vehicle, the state of driving such as driving or stopping is also meaningful. For example, if the vehicle does not decelerate even when the brake is depressed during traveling, it can be said that the vehicle has a problem. Further, while the vehicle is stopped, the vehicle is stopped by stepping on the brake, hand brake, or parking brake. That is, the driving state can be determined from the operation item. The operation item can be acquired from the signal data as a signal item.

  The operation item is associated with the performance of the vehicle, for example, the speed measured by the instrument. The operation items are associated with operational characteristics such as the driver's driving characteristics, situation and environmental characteristics, and vehicle operating time.

  FIG. 15 is a diagram showing an example of the relationship between monitor items and operation items, and FIG. 16 is a diagram showing an example of the relationship between operation items and characteristics in FIG. Driving characteristics, situation / environmental characteristics, and operational characteristics are subject to vehicle diagnosis. For example, the content of “causes many sudden accelerations to cause deterioration of fuel consumption” is a diagnosis related to driving characteristics. In view of these, a vehicle malfunction is also diagnosed. Further, the “monitor (instrument) item” shown in FIG. 15 is a signal item, and is used for vehicle diagnosis in the same manner as the “operation item”.

  The driving state is particularly related to the “driving characteristics” shown in FIG. The manner of driving by the driver is based on the operation while traveling. In addition, the driving state such as when the vehicle is stopped can be interpreted as an environmental characteristic such as congestion, and also as an operational characteristic such as idling.

  Therefore, the vehicle diagnosis device 20 diagnoses the driving state of the vehicle before vehicle diagnosis. The diagnosis of the driving state is performed by the driving state diagnosis processing unit 22b. The driving state diagnosis processing unit 22b refers to the driving state determination rule stored in the driving state determination rule DB 24d when the driving state is diagnosed.

  The driving state is assumed to be a vehicle state determined by an operation by the driver, such as when the vehicle is stopped or driving. The driving state can be determined by, for example, a combination of driving operations, and the driving operation can be identified by the presence / absence of the operation of the “operation item” illustrated in FIGS. 15 and 16. The “operation item” is a signal item and can be acquired as signal data.

  Therefore, the driving state of the vehicle can be diagnosed from the signal feature amount. The presence or absence of the operation can be determined symbolically from the signal feature rank determination result and the rank determination result. In addition to engine, brake, and steering operations, stepwise operations such as neutral speed and first speed of the transmission can be acquired as signals.

  The signal data is not necessarily a numerical value but may be a character code or symbolic data. That is, the driving state diagnosis processing unit 22b may diagnose the driving state by a combination of signal feature amounts. The rule for this is called a driving state determination rule.

  An example of the driving state determination rule is shown below. The result of the rank determination is simply {Yes, No}. In the following example, the format is “presence / absence of operation item or operation state: operation state”. There are a plurality of “operation item presence / absence or operation state”, and it is assumed that the operation state is determined when all are satisfied (logical product). That is, it is an IF-THEN rule format in which “the presence / absence of an operation item or driving state” is a condition and “driving state” is a result.

(Engine running, Yes), (Hand brake, Yes): Stopped (Engine running, Yes), (Parking, Yes): Stopped (Engine running, Yes), (Hand brake, No): Running (Engine running) , Existence), (Parking, No): While traveling, (Accelerator stepping, Existence): Acceleration During travel, (Steering right turn, Existence): During right turn Traveling (Back speed, Existence): Back

  FIG. 17 is a diagram illustrating a transition example of the operation state. While the vehicle is stopped, there are a key-released state and an engine-stopped state. Further, when the vehicle is stopped, the vehicle is idling and there is a state where the hand brake, the parking, or the pedal brake is used. When the vehicle moves, the vehicle is traveling backward or traveling forward. Further, the steering wheel is operated to turn right or turn left. During traveling, the operation shifts from neutral to first speed and second speed by operating the transmission. Operations such as braking, accelerator, steering, lighting, and wiper may also be defined as the vehicle state. The operating state is not limited to that shown in FIG.

  If the driving state determination rule is incomplete or there is no signal item corresponding to the operation, the condition for determining the driving state may not be determined or satisfied. In such a case, the operating state may be unknown. The driving state is a means for enriching the contents of diagnosis, and is not essential for diagnosis. The above is the description of the operating state.

  An example of the rank determination result output by the determination processing unit 22 will be described.

  FIG. 18 is a diagram illustrating a data configuration example of the rank determination result. The rank determination result has a set (negative instruction, time point, time range | end time point, operation state, monitor item | operation item, calculation method, signal feature, state). If indicated by a symbol, the rank determination result has (fn, tm, tr | te, sd, im | ia, mc, cs, sj). The vertical bar “|” is a selective calculation of “any”. If “a | b”, it is “a” or “b”. The set (a | b, c | d) is any set of (a, b), (a, c), (b, c), and (b, d).

  The negative instruction “fn (not flag)” is identification information indicating whether the state “sj” indicating the result of the rank determination is affirmed or denied, and is a binary boolean. For example, if the rank determination is two ranks of {NORMAL, ABNORMAL}, if “NORMAL” is affirmed, “ABNORMAL” is denied. In the rank determination, since the corresponding rank is used as the determination result, the result is basically a positive result. The negative determination result may be negated by a negative instruction “fn” with a rank that is not applicable in the rank determination as a determination result. Whether a negative determination result is required depends on the ease of description of the diagnostic rule in the diagnostic processing. If unnecessary, only a positive result may be determined.

The time “tm (time)” means the start time “t ^st ”. The data format at a specific time point may be year / month / day / hour / minute / second “yyyy / MM / dd HH: mm: ss.SSS” or may be a numerical value in a predetermined unit from the reference time point. If the unit is seconds, the number is seconds. The number of digits may be limited, whether it is an integer or a decimal, but it depends on the equipment such as a computer and sensor and the implementation of the program.

  In order to determine the time range of the rank determination result, the rank determination result includes either a time range “tr (time range)” or an end time “te (end time)”. The time range “tr” may be set to a numerical value in a predetermined unit. The end time “te” may have the same data format as the time “tm”.

  The driving state “sd (driving status)” is obtained by the driving state diagnosis processing unit 22b. The operation state “sd” is, for example, the state illustrated in FIG.

  The monitor item “io (monitored item)” and the operation item “ia (manipulated item)” mean signal items. This corresponds to the monitor items and operation items shown in FIGS.

The calculation method “mc (calculation method)” is an item related to a dividing method such as signal feature division of synthesis of change non-division and changeable division or individual change non-division and changeable division. The signal feature amount is a line segment divided by the signal feature rank determination, and the above-described method is not necessarily required to divide in this way. For example, if the signal feature rank determination result is in the range of adjacent ranks, no change is made. The rank may be a rank at the start time “t ^st _#i ” or a rank corresponding to an average of the data value at the start time “t ^st _#i ” and the data value at the end time “t ^en _#i ”.

  The signal feature “cs” means a rank determination target. The signal feature “cs” includes, for example, the signal feature value itself, the length (time range) of the signal feature value, the upper change, the lower change, the upward slope, the downward slope, and the average, standard deviation, minimum, and maximum of the total results , Etc.

  The state “sj” is a rank determination result. The state “sj” may be a signal feature rank determination result in the case of the signal feature amount itself. The above is the description of the rank determination result.

  The vehicle diagnosis will be described. The vehicle diagnosis is performed by the diagnosis processing unit 23. The determination result acquisition unit 23a of the diagnosis processing unit 23 acquires the rank determination result (for example, FIG. 18) of the determination processing unit 22, and the diagnosis unit 23b is based on the rank determination result acquired by the determination result acquisition unit 23a. Car diagnosis is performed. At that time, the diagnosis unit 23b performs vehicle diagnosis by an inference process using the diagnosis rule stored in the diagnosis rule 24c.

  The diagnosis rule is a rule having a logical structure that derives a diagnosis result from the rank determination result and the diagnosis result. The diagnosis unit 23b performs an inference that derives the diagnosis result from the rank determination result and further derives the diagnosis result from the diagnosis result. In logic, deriving another proposition from a proposition is called reasoning. That is, the diagnosis unit 23b treats the rank determination result and the diagnosis result as propositions. However, the rank determination result is accompanied by a state that is the result of rank determination. Further, the diagnosis result is a content meaning a phenomenon such as a defect, and the diagnosis content is accompanied by a state as a degree of the defect.

  An example in which a diagnostic rule is defined in the form of an IF-THEN rule is shown. The diagnosis rule is expressed by the following formula (3). The diagnosis rule is stored in the diagnosis rule DB 24c.

  “A” in Expression (3) is an individual condition for the determination result or diagnosis result, and “B” is the diagnosis result. “T_rel” after “IF” in Expression (3) is a designation of a condition regarding a time relationship of signal feature values. Also called time relation designation, time relation instruction, or time relation condition. The part after “THEN” in the formula (3) means “not B or B”. The “IF” part is a condition part, and the “THEN” part is a result part. The condition part has one or more conditions. That is, “n” is 1 or more. Results are obtained when all conditions are met. It is said to ignite that all conditions of the diagnostic rule are satisfied.

  The time relationship between the signal feature amounts is, for example, a relationship between two signal feature amounts of the same signal item, that is, continuous or the first condition is before the second condition. Between different signal items, a relationship due to overlapping time ranges is also defined. It is defined as 3 continuous, 4 continuous, etc. for multiple conditions.

  One condition “A” is a rank determination result or a diagnosis result. The rank determination result is indicated by, for example, a set (negative instruction, time point, time range | end point, operation state, monitor item | operation item, calculation method, signal feature, state) as shown in FIG. Anything can be used as long as it can determine whether the contents of the rank determination results match. The rank determination is numerically determined and has a range in which the value decreases and increases. Accordingly, the range can be determined based on the reference state serving as a reference and the state ranges of the other states above or below or above and below. Regarding the time, like the determination, the time range for diagnosis is determined as the target time range, and the range is not limited by the rule itself. Therefore, the condition for the rank determination result can be a set (negative instruction, operation state, monitor item | operation item, calculation method, signal feature, reference state, state range). The state of the rank determination result is {A, B, C, D, E}. If the reference state is “C” and the state range is “NOT_LESS” or more, the range is {C, D, E}, “NOT_MORE” is {A, B, C}, “B” is {B, C}, “E ", Then {C, D, E}. If it is “C”, it is {C}. If it is limited to the reference state, the reference state is repeated or the match “JUST” is designated. That is, {C} for "C" or "JUST". In the expression (3), a negative symbol is described outside “A, B”, which means a negative instruction for a set element.

  In the diagnosis result, it is assumed that the content such as a character symbol, a sentence, and a sentence is set as a character string. Others carry over the information on the determination results and diagnosis results. The diagnosis result is a set (negative instruction, time point, time range | end point, operation state, result, diagnosis result state). Among these, for example, a result such as “brake failure” is not related to the driving state, and therefore the content of the driving state may be empty or not. The diagnosis result is a content about a malfunction or abnormality. Therefore, the state of the diagnosis result is {NORMAL, NEGLIGABLE, MARGINAL, CRITICAL, CATASTROPHIC} or {NORMAL, CAUTION, WARNING, ALERT, URGENT, DANGER, CRITICAL}. Note that the set is terrible and bad in order of ascending order.

  The condition for the diagnosis result is a set (negative instruction, operation state, result, reference diagnosis result state, diagnosis result state range). The reference diagnosis result state and the diagnosis result state range are the same as the reference state and state range of the condition for the determination result.

  As a result of applying the diagnosis rule, a diagnosis result state is set in the diagnosis result. When the diagnosis result is derived using only the diagnosis result as a condition, the diagnosis result state of the diagnosis result that satisfies the condition can be set as the diagnosis result state of the diagnosis result. However, when the diagnosis result is derived using the determination result as a condition, the rank states are different. In this case, the diagnosis result state of the derived diagnosis result may be assigned to the determination result state. For example, in the case of the determination result state {LOW, MEDIUM, HIGH}, “NORMAL” for “LOW”, “MARGINAL” for “MEDIA”, and “CATASTROPIC” for “HIGH”. Therefore, information on correspondence is added to the result part of the diagnosis rule. Alternatively, the diagnosis result state of the result diagnosis result is set as fixed. Alternatively, a list of correspondences between the states of all the ranks to be used is created, and the condition part has the highest degree of judgment result state and diagnosis result state corresponding to the diagnosis result. A method of setting the result as a diagnosis result state may be used. The above is the description of the diagnostic rule.

  An example of vehicle diagnosis will be described.

  FIG. 19 is a diagram illustrating an example of signal feature amounts to be diagnosed. It is assumed that the feature division processing unit 13 of the data acquisition device 10 divides the signal data acquired by the signal data acquisition unit 12 into signal feature amounts as shown in FIG. The signal feature amount (signal feature amount shown in FIG. 19) divided by the feature division processing unit 13 is transmitted to the vehicle diagnostic apparatus 20 by the communication processing unit 11. The example of the signal feature amount shown in FIG. 19 is a graph, and is actually transmitted to the vehicle diagnosis apparatus 20 in the data format as shown in FIG.

  The signal feature amount transmitted to the vehicle diagnostic apparatus 20 is subjected to rank determination processing by the determination processing unit 22. For example, the determination processing unit 22 generates a rank determination result in the data format as illustrated in FIG. 18 from the signal feature amount illustrated in FIG.

  In addition, “A, B, C, D” shown on the right side of FIG. 19 indicates a signal feature rank. Further, the signal item of the signal data shown in FIG. 19 is the engine speed.

  Assume that the diagnostic rule shown in the following equation (4) is stored in the diagnostic rule DB 24c for the signal item “engine speed” shown in FIG.

  IF () (Affirmation, Running, Engine speed, Signal feature division, Total_Generation number, URGENT, NOT_LESS) THEN (Affirmation, Running, Accelerator operation) (4)

  The time relation instruction “t_rel” is empty “()”. Assume that the state of the determination result corresponding to the condition is inherited.

The rank determination state {NORMAL, LOW, MEDIUM, HIGH} is assumed to have a one-to-one correspondence with the signal feature rank determination rank {A, B, C, D} (see the right side of FIG. 19). In addition, it is assumed that the number of appearances of the signal feature rank determination result “D” is 3 times or more and less than 6 with respect to the signal feature amount summation result, and the “URGENT” rank determination result is obtained. In addition, the time range for rank determination is the same as the time range for aggregation, and is assumed to be “T ^st = t1” and “T ^en = t17” (see “Time” on the horizontal axis in FIG. 19). In this case, for example, the determination processing unit 22 generates the next rank determination result “R1” according to the data format shown in FIG.

  (Affirmation, t1, t17, traveling, engine speed, signal feature division, count_number of occurrences, URGENT) (R1)

  When this rank determination result “R1” is applied to the diagnosis rule of Expression (4), the following diagnosis result is obtained.

  (Yes, t1, t17, traveling, many accelerator operations, URGENT)

  Further, it is assumed that the diagnostic rule shown in the following equation (5) is stored in the diagnostic rule DB 24c for the signal item “engine speed” shown in FIG.

  The time relation instruction “t_rel” is three consecutive. For example, the following rank determination results “R11 to R13” are obtained from the signal feature amounts of three consecutive “feature 1, feature 2, feature 3”.

(Yes, t1, t2, traveling, engine speed, signal feature division, signal feature amount, NORMAL) (R11)
(Yes, t2, t3, running, engine speed, signal feature division, time range, SHORT)
... (R12)
(Affirmation, t3, t4, traveling, engine speed, signal feature division, signal feature, HIGH)
... (R13)

  When the rank determination results “R11 to R13” are applied to the diagnosis rule of Expression (5), the following diagnosis result is obtained.

  (Affirmation, t1, t4, driving, sudden accelerator depression, ALERT)

  Note that the same diagnosis result is derived for “feature 13, feature 14, and feature 15” shown in FIG.

  (Affirmation, t14, t17, while driving, sudden accelerator depression, ALERT)

  The above is the description of the diagnosis using the example of the signal feature amount of FIG.

  An example of vehicle diagnosis using two signal items will be described.

  FIG. 20 is a diagram illustrating an example of signal feature amounts of two signal items to be diagnosed. FIG. 20A shows an example of the signal feature amount of the signal item “speed”, and FIG. 20B shows an example of the signal feature amount of the signal item “brake depression”. .

  The speed signal feature amount is from feature s1 to feature s9, and the brake step signal feature amount is from feature b1 to feature b10. The rank determination state is {CRAWL, SLOW, MEDIUM, HIGH} for speed, and {NORMAL, LOW, MEDIUM, HIGH} for brake depression.

  Assume that the diagnosis rule shown in the following equation (6) is stored in the diagnosis rule DB 24c for the signal items “speed” and “brake depression” shown in FIG.

  The time relation instruction “t_rel” is “time overlap”. The signal item in FIG. 20A is “speed”, and the rank is determined by obtaining the inclination. The state of the inclination rank determination is {NO_CHANGE, SLIGHT, SMALL, MEDIUM, LARGE}. The feature s1, the feature s4, the feature s6, and the feature s8 are “NO_CHANGE” as both the upward slope and the downward slope, the feature s2 and the feature s5 are “LARGE” as the upward slope, and the feature s3 is “LARGE” as the downward slope. The feature s7 and the feature s9 are “SLIGHT” as the downward inclination.

  First, it is the feature s1, the feature s4, the feature s6, the feature s7, the feature s8, and the feature s9 that satisfy the first condition regarding the speed of the diagnostic rule of Expression (6). On the other hand, it is the features b3 and b10 that satisfy the second condition regarding the brake depression. Here, it is assumed that the signal feature amount with the change of the feature b2, the feature b4, the feature b6, and the feature b9 is not determined as “MEDIAUM” or more.

  Due to overlapping time ranges, the following combinations of rank determination results “R21” and “R22” and combinations of “R23” and “R24” satisfy the condition of the diagnostic rule of Expression (6).

(Affirmation, t11, t13, traveling, speed, signal feature division, downward tilt, SLIGHT)
... (R21)
(Affirmation, t11, t12, traveling, brake depression, signal feature division, signal feature amount, MEDIUM) (R22)
(Affirmation, t16, t17, traveling, speed, signal feature division, downward tilt, SLIGHT)
... (R23)
(Affirmation, t15, t17, during travel, brake depression, signal feature division, signal feature amount, HIGH) (R24)

  Furthermore, there is a relationship of “t15 <t16” in overlapping time ranges, and the rank determination results “R25” and “R24” of the feature s8 and the feature b10 also satisfy the diagnosis rule of Expression (6).

  (Yes, t13, t16, traveling, speed, signal feature division, downward inclination, NO_CHANGE) (R25)

  The following diagnostic results “D1 to D3” are obtained in the order of combinations shown above.

(Affirmation, t11, t12, traveling, brake effect abnormality, URGENT) (D1)
(Affirmation, t16, t17, traveling, brake effect abnormality, URGENT) (D2)
(Affirmation, t14, t15, traveling, braking effectiveness abnormality, URGENT) (D3)

  The time range is, for example, “t14” to “t15” in the diagnosis result “D3”, “t13” to “t16” in the rank determination result “R25”, and “t15” to “t17” in the rank determination result “R25”. ”And the overlapping range (product range). The time range may be a range from “t13” to “t17” and at least one range (sum range) that satisfies the condition. Although the time range instruction is time overlap, it may be clarified as product range time overlap or sum range time overlap. The above is the description of the diagnosis using the example of the signal feature amount of FIG.

  A diagnosis example for deriving the diagnosis result from the diagnosis result will be described. Diagnosis needs to lead to good machine operation, deterioration of parts, malfunctions, failures, and the quality and characteristics of the driver's driving. To guide the diagnostic results according to the purpose.

  The diagnosis result is determined by a set (negative instruction, time point, time range | end point, operation state, result, diagnosis result state), but in the description here, it is only necessary to express the relationship of only the contents of the diagnosis result. Therefore, the description is omitted. An example of the diagnosis result “D11” is shown below.

  (Affirmation, ts, te, accelerator amount, CRITICAL) (D11)

  The time point “ts” and the end time point “te”, that is, the time information and the diagnostic result state “CRITICAL” are handled in a variable manner like variables. The handling of time information and state is as described in FIGS. 19 and 20, and the diagnosis rule is shown only as a result of the diagnosis result. The diagnosis result “D11” is assumed to be “the accelerator amount is large”. The diagnostic rule of the IF-THEN rule is described as “condition part → result part”. Negative instructions are indicated by a negative sign.

  Examples of diagnostic rules for diagnosing driver characteristics and wrinkles are shown in Equations (7) to (9).

  Since the diagnosis result derived by the diagnosis rule such as Expression (7) to Expression (9) has an influence on the malfunction, it can guide the operation method for preventing such operation. According to the diagnostic rule of equation (8), it is effective to gradually accelerate and shift according to speed. In addition, according to such driving habits, it is possible to tune the machine, select parts such as lubricating oil, and recommend maintenance time. In the case of the diagnostic rule of formula (9), if the vehicle is for business use and is frequently driven alternately in the city and at high speed, it is better to increase the frequency of inspection and maintenance.

  Examples of diagnostic rules for diagnosing machine malfunctions and abnormalities are shown in equations (10) to (12).

  A vehicle is composed of a system such as a steering (steering) device, a braking device, a traveling device, a shock absorber, and a prime mover, and each device is composed of parts. Therefore, the vehicle diagnosis apparatus 20 can perform diagnosis for specifying an abnormal part. If an abnormality can be detected, the driver or the vehicle owner can determine whether maintenance is necessary by warning the driver or the vehicle owner. On the other hand, in the service company 5 that performs maintenance, maintenance, parts sales, etc. shown in FIG. 1, it is possible to handle maintenance schedules, arrangement of maintenance shops, confirmation of necessary parts inventory, parts procurement, and the like. Diagnostic rules for these can also be defined. The above is an explanation of a diagnosis example for deriving a diagnosis result from a diagnosis result.

  Describe diagnostic reports. The report generation unit 25 generates a diagnosis report based on the diagnosis result of the diagnosis processing unit 23.

  FIG. 21 is a diagram showing an example of a diagnosis report. The diagnostic report is displayed on a display device, for example. As shown in FIG. 21, the diagnosis report includes a determination result and a diagnosis result. The diagnosis report includes a totaling period.

  In the judgment result, the item, category, value, and influence are used as column headings. Only the total results are displayed. The item is a signal item, the classification is the content of the summary calculation, and the influence is the state of rank determination. Although individual signal feature amounts used for diagnosis in the diagnosis rule may be displayed, the amount is enormous and cannot be essential for the diagnosis report.

  The diagnosis result is directly displayed when the sentence assigned to the vehicle diagnosis result, the sentence generated using the vehicle diagnosis result, or the result of the vehicle diagnosis result is a sentence. The diagnosis results are displayed separately for operation and vehicle status. The operation corresponds to the driver's bag, and the vehicle condition corresponds to a machine malfunction.

  In the operation, a sentence corresponding to the diagnosis result derived from the determination result of the travel distance and the fuel consumption is displayed. In the operation, a sentence corresponding to the diagnosis result derived from the high frequency of the speed, the high frequency of the rotation speed, and the low frequency is displayed. In the operation, a diagnosis result derived from a diagnosis result of fuel consumption and a diagnosis result of speed change is displayed, and the content is a sentence corresponding to the recommendation.

  In the vehicle state, a sentence corresponding to the diagnosis result derived from the travel distance is displayed. In the vehicle state, a sentence corresponding to the diagnosis result derived from the fuel consumption is displayed.

  The diagnosis report is not composed of only the above items, but can also include customer information such as the driver and owner, and vehicle information. Also, a graphical representation for confirming time-series fluctuations may be shown.

  The diagnosis report may be a screen displayed directly on a portable terminal such as a display or a smartphone as well as outputting a text data file such as an Adobe pdf file or a Microsoft doc file. This completes the explanation of the diagnostic report.

  FIG. 22 is a flowchart showing a processing example of the signal division method. 23 to 25 are flowcharts showing examples of diagnosis processing. FIG. 23 is a flowchart showing the entire diagnosis process. The processing between “1” and “2” circled in FIG. 23 is shown in FIGS. 24 and 25. “1” in FIG. 23 is connected to “1” in FIG. 24, and “2” in FIG. 25 is connected to “2” in FIG. 24 and 25 are connected to a circled “3”.

  FIG. 26 is a diagram illustrating a hardware configuration example of the vehicle diagnosis apparatus 20. The vehicle diagnostic apparatus 20 includes, for example, an arithmetic device 101 such as a CPU (Central Processing Unit), a main storage device 102 such as a RAM (Random Access Memory), and an auxiliary storage device 103 such as an HDD as shown in FIG. A communication interface (I / F) 104 for connecting to a communication network by wire or wireless, an input device 105 such as a mouse, keyboard, touch sensor or touch panel, a display device 106 such as a liquid crystal display, and a DVD (Digital Versatile It can be realized by a computer including a read / write device 107 that reads / writes information from / to a portable storage medium such as a Disc) or an SSD (Solid State Drive).

  The function of each part of the vehicle diagnostic device 20 is realized by the arithmetic device 101 executing a predetermined program loaded from the auxiliary storage device 103 or the like to the main storage device 102, for example. Further, each storage unit of the vehicle diagnosis device 20 is realized, for example, when the arithmetic device 101 uses the main storage device 102 or the auxiliary storage device 103.

  The predetermined program may be installed from a storage medium read by the read / write device 107 or may be installed from a network via the communication I / F 104, for example. The data acquisition device 10 can also be configured by the hardware shown in FIG.

  As described above, the vehicle diagnosis apparatus 20 uses the vehicle signal data that is time-series data, and the vehicle diagnosis apparatus 20 ranks the signal data in an arbitrary period for each signal item. 22 and a diagnosis processing unit 23 for diagnosing the state of driving and vehicle based on the diagnosis rule having a logical structure from the rank determination result in the signal item. Signal data is converted into symbol data using signal feature determination management reference information for dividing into signal feature values, and is divided into signal feature values with a time range based on a series of time series of symbol data. The 20 determination processing units 22 total the occurrences of the signal feature amounts, determine the rank of the total result using the rank determination management reference information, or use the symbol data itself as the rank determination result. Thereby, the vehicle diagnostic apparatus 20 can extract the time-series change pattern of the signal data as a feature amount and appropriately diagnose the vehicle state.

  In the above description, the data acquisition device 10 includes the feature division processing unit 13 and the signal feature determination management reference information DB 14, but the vehicle diagnosis device 20 may include these. In this case, the communication processing unit 11 of the data acquisition device 10 transmits the signal data acquired by the signal data acquisition unit 12 to the vehicle diagnostic device 20. Then, the vehicle diagnosis device 20 divides the signal data transmitted from the data acquisition device 10 into signal feature amounts.

  In the above description, the vehicle diagnosis apparatus 20 included in the data center 1 generates the diagnosis report. However, the processes up to generation and transmission of the diagnosis report may be performed in the vehicles 2 and 3. In this case, the vehicle diagnostic apparatus 20 illustrated in FIG. 2 includes the functional blocks of the data acquisition apparatus 10. The vehicle diagnostic device 20 having the functional block of the data acquisition device 10 is mounted on the vehicles 2 and 3.

  The functional configurations of the data acquisition device 10 and the vehicle diagnosis device 20 described above are classified according to main processing contents in order to facilitate understanding of the configurations of the data acquisition device 10 and the vehicle diagnosis device 20. The present invention is not limited by the way of classification and names of the constituent elements. The configurations of the data acquisition device 10 and the vehicle diagnosis device 20 can be further classified into more components according to the processing content. Moreover, it can also classify | categorize so that one component may perform more processes. Further, the processing of each component may be executed by one hardware or may be executed by a plurality of hardware. Further, the data acquisition device 10 and the vehicle diagnosis device 20 may not be a computer that is executed by performing arithmetic processing with a so-called CPU, but may be mounted on a device as an embedded microcomputer board.

  In addition, each processing unit in the flowchart described above is divided according to main processing contents in order to facilitate understanding of the processing of the data acquisition device 10 and the vehicle diagnosis device 20. The present invention is not limited by the way of dividing the processing unit or the name. The processing of the data acquisition device 10 and the vehicle diagnosis device 20 can be divided into more processing units depending on the processing content. Moreover, it can also divide | segment so that one process unit may contain many processes. The present invention can also be provided as a program that realizes the functions of the data acquisition device 10 and the vehicle diagnosis device 20, and a storage medium that stores the program.

  Each of the above-described configurations, functions, processing units, and the like may be realized by hardware, for example, by designing a part or all of them with an integrated circuit. Further, the control lines and information lines indicate what is considered necessary for the explanation, and not all the control lines and information lines on the product are necessarily shown. Actually, it may be considered that almost all the components are connected to each other.

  Further, the technical elements of the above-described embodiments may be applied independently, or may be applied by being divided into a plurality of parts such as program parts and hardware parts.

  DESCRIPTION OF SYMBOLS 1 ... Data center, 2, 3 ... Vehicle, 4 ... Customer company, 5 ... Service company, 10 ... Data acquisition apparatus, 11 ... Communication processing part, 12 ... Signal data acquisition part, 13 ... Feature division processing part, 14 ... Signal Feature determination management reference information DB, 20 ... vehicle diagnostic device, 21 ... communication processing unit, 22 ... determination processing unit, 22a ... signal feature aggregation processing unit, 22b ... driving state diagnosis processing unit, 22c ... rank determination processing unit, 23 ... Diagnosis processing unit, 23a ... determination result acquisition unit, 23b ... diagnosis unit, 24 ... storage unit, 24a ... feature amount data DB, 24b ... rank determination management reference information DB, 24c ... diagnosis rule, 24d ... driving state determination rule DB, 25: Report generation unit.

Claims (9)

A vehicle diagnostic apparatus using vehicle signal data that is time-series data generated with the passage of time ,
Using the signal feature determination management reference information in which a threshold value for associating the signal value of the signal data with the first rank using a predetermined symbol is stored, the signal data is stored for each item of the signal data. A signal feature amount divided from the signal data as information including the symbolized data converted into the symbol indicating the rank of the symbol, and a time range partitioned based on the presence or absence of a change in the time series of the symbolized data. Tally and
Based on the summation result of the signal feature amount, the symbolized data is associated with a second rank different from the first rank, and the second rank or the symbolized data is used to determine the item of the signal data. A determination processing unit that performs rank determination for determining driving and vehicle state in the time range, and outputs a result of the rank determination as a rank determination result ;
Said rank determination result of entry of the signal data, based on the diagnostic rule having a logical structure, a vehicle diagnosis according to claim <br/> further comprising a diagnostic processor for diagnosing the state of operation and the vehicle, a apparatus. The vehicle diagnostic apparatus according to claim 1,
The rank determination result is negative indication, the time range, the item of the signal data, characterized division calculation method, signal characteristics, constituted by and states, each item,
The negative instruction is identification information indicating whether the state indicating the rank determination result is positive or negative,
The time range is a time range of the signal feature amount,
The item of the signal data is an operation item related to the item of the monitor instrument and the performance of the vehicle measured by the monitor instrument, the driving characteristic of the driver, the situation and the environmental characteristic, and the operational characteristic of the vehicle,
The feature division calculation method is a method of dividing the signal data into the signal feature amounts,
The signal feature is an object of the rank determination,
The vehicle diagnosis apparatus characterized in that the state is the state of the driving and vehicle indicated by the rank determination result . The vehicle diagnostic apparatus according to claim 2 ,
The determination processing unit obtains a driving state based on a driving state determination rule for diagnosing a driving state, and includes the driving state in the rank determination result.
A vehicle diagnostic apparatus. The vehicle diagnostic apparatus according to claim 2 ,
The diagnostic rule is a condition for one or more of the rank determination result or diagnostic results I Oh the rule showing a relationship leading to a diagnosis result from one or more of the rank determination result or the diagnosis result, guide Represented by a predetermined arithmetic expression including the contents of the diagnosis result
One condition of the diagnostic rules for guiding the diagnosis result from the rank determination result, the negative indication, items of the signal data, the feature division calculation method arrangement, the signal characteristics, the state range of the rank determination result And
From the diagnosis result, one of the conditions of the diagnostic rules for guiding the diagnosis result, the negative indication, the result is constituted by the state range of the rank determination result,
The diagnostic results derived, the results and the configured with the negative indication,
A vehicle diagnostic apparatus. The vehicle diagnostic device according to claim 4 ,
The condition of the diagnostic rule, the conditions with respect to time relationship for the one or more conditions of the rank determination result and diagnostic result refers to the time relationship of the signal feature value is specified,
The diagnostic processor is-out based on the diagnostic rule, for one or more conditions, the rank determination result, or diagnostic results, determines whether to satisfy the mutual time range relationship is satisfied rather guide the cross-sectional results seen when,
A vehicle diagnostic apparatus. The vehicle diagnostic apparatus according to claim 5 ,
The condition is related to the time relationship for two or more conditions of the rank determination result and diagnostic result, the condition that specifies more than one single item of the signal data or diagnostic results time context, ,
A vehicle diagnostic apparatus. The vehicle diagnostic apparatus according to claim 5 ,
The condition is related to the time relationship for two or more conditions of the rank determination result and diagnostic result is a condition for specifying the two or more different the items of signal data or diagnostic results of time overlapping relationship,
A vehicle diagnostic apparatus. The vehicle diagnostic apparatus according to claim 1,
The division of the signal data into the signal feature values is processed in a vehicle.
A vehicle diagnostic apparatus. The vehicle diagnostic apparatus according to claim 1,
A vehicle diagnostic apparatus , further comprising a feature division processing unit that divides the signal data into the signal feature values.

Images